Self-Managing Database System Using Machine Learning

2. The system of claim 1, wherein the metrics data comprises at least one of database metrics and operating system metrics.

3. The system of claim 1, wherein the metrics data comprises at least one of log scanner metrics and change scanner metrics.

4. The system of claim 1, wherein the metrics collector is to store metrics data collected within a most recent selected period of time as recent metrics data in a memory of the computing system and metrics data collected earlier than the most recent selected period of time as historical metrics data in a long term storage device of the computing system.

5. The system of claim 4, wherein the anomaly detector is to compute a median value, a median absolute deviation, and a critical threshold for a metric of the historical metrics data.

7. The system of claim 6, wherein the anomaly detector is to mark the sample as anomalous when an absolute value of the Z score of the sample is greater than an absolute value of the critical threshold.

8. The system of claim 7, wherein the anomaly detector is to mark the metric as anomalous when a number of anomalous samples for the metric is greater than a predetermined threshold.

9. The system of claim 1, wherein the knowledge representation comprises a Bayesian network.

12. The computer-implemented method of claim 11, wherein the metrics data comprises at least one of database metrics and operating system metrics.

13. The computer-implemented method of claim 11, wherein the metrics data comprises at least one of log scanner metrics and change scanner metrics.

14. The computer-implemented method of claim 11, comprising storing metrics data collected within a most recent selected period of time as recent metrics data in a memory of the computing system and metrics data collected earlier than the most recent selected period of time as historical metrics data in a long term storage device of the computing system.

15. The computer-implemented method of claim 14, comprising computing a median value, a median absolute deviation, and a critical threshold for a metric of the historical metrics data.

17. The computer-implemented method of claim 16, comprising marking the sample as anomalous when an absolute value of the Z score of the sample is greater than an absolute value of the critical threshold.

18. The computer-implemented method of claim 17, comprising marking the metric as anomalous when a number of anomalous samples for the metric is greater than a predetermined threshold.

19. The computer-implemented method of claim 11, wherein the knowledge representation comprises a Bayesian network.

22. The tangible, non-transitory computer-readable storage medium of claim 21, comprising instructions to store metrics data collected within a most recent selected period of time as recent metrics data in a memory of the computing system and metrics data collected earlier than the most recent selected period of time as historical metrics data in a long term storage device of the computing system.

23. The tangible, non-transitory computer-readable storage medium of claim 22, comprising instructions to compute a median value, a median absolute deviation, and a critical threshold for a metric of the historical metrics data.

25. The tangible, non-transitory computer-readable storage of claim 24, comprising instructions to mark the sample as anomalous when an absolute value of the Z score of the sample is greater than an absolute value of the critical threshold.

26. The tangible, non-transitory computer-readable storage medium of claim 25, comprising instructions to mark the metric as anomalous when a number of anomalous samples for the metric is greater than a predetermined threshold.

27. The tangible, non-transitory computer-readable storage medium of claim 21, wherein the knowledge representation comprises a Bayesian network.